85 research outputs found
EdgeFaaS: A Function-based Framework for Edge Computing
The rapid growth of data generated from Internet of Things (IoTs) such as
smart phones and smart home devices presents new challenges to cloud computing
in transferring, storing, and processing the data. With increasingly more
powerful edge devices, edge computing, on the other hand, has the potential to
better responsiveness, privacy, and cost efficiency. However, resources across
the cloud and edge are highly distributed and highly diverse. To address these
challenges, this paper proposes EdgeFaaS, a Function-as-a-Service (FaaS) based
computing framework that supports the flexible, convenient, and optimized use
of distributed and heterogeneous resources across IoT, edge, and cloud systems.
EdgeFaaS allows cluster resources and individual devices to be managed under
the same framework and provide computational and storage resources for
functions. It provides virtual function and virtual storage interfaces for
consistent function management and storage management across heterogeneous
compute and storage resources. It automatically optimizes the scheduling of
functions and placement of data according to their performance and privacy
requirements. EdgeFaaS is evaluated based on two edge workflows: video
analytics workflow and federated learning workflow, both of which are
representative edge applications and involve large amounts of input data
generated from edge devices
Regularized Robust MDPs and Risk-Sensitive MDPs: Equivalence, Policy Gradient, and Sample Complexity
This paper focuses on reinforcement learning for the regularized robust
Markov decision process (MDP) problem, an extension of the robust MDP
framework. We first introduce the risk-sensitive MDP and establish the
equivalence between risk-sensitive MDP and regularized robust MDP. This
equivalence offers an alternative perspective for addressing the regularized
RMDP and enables the design of efficient learning algorithms. Given this
equivalence, we further derive the policy gradient theorem for the regularized
robust MDP problem and prove the global convergence of the exact policy
gradient method under the tabular setting with direct parameterization. We also
propose a sample-based offline learning algorithm, namely the robust fitted-Z
iteration (RFZI), for a specific regularized robust MDP problem with a
KL-divergence regularization term and analyze the sample complexity of the
algorithm. Our results are also supported by numerical simulations
When Edge Meets FaaS: Opportunities and Challenges
The proliferation of edge devices and the rapid growth of IoT data have
called forth the edge computing paradigm. Function-as-a-service (FaaS) is a
promising computing paradigm to realize edge computing. This paper explores the
feasibility and advantages of FaaS-based edge computing. It also studies the
research challenges that should be addressed in the design of such systems,
which are 1) the quick decomposing and recomposing of applications, 2) the
trade-off between performance and isolation of sandbox mechanisms, and 3)
distributed scheduling. The challenges are illustrated by evaluating existing
FaaS-based edge platforms, AWS IoT Greengrass, and OpenFaaS
On the Relationship of Optimal State Feedback and Disturbance Response Controllers
This paper studies the relationship between state feedback policies and
disturbance response policies for the standard Linear Quadratic Regulator
(LQR). For open-loop stable plants, we establish a simple relationship between
the optimal state feedback controller and the optimal
disturbance response controller
with -order.
Here stands for the state, disturbance, control action of the
system, respectively. Our result shows that is a good
approximation of and the approximation error decays exponentially with . We further extend this
result to LQR for open-loop unstable systems, when a pre-stabilizing controller
is available
DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Indoor Semantic Segmentation
Deep learning approaches achieve prominent success in 3D semantic
segmentation. However, collecting densely annotated real-world 3D datasets is
extremely time-consuming and expensive. Training models on synthetic data and
generalizing on real-world scenarios becomes an appealing alternative, but
unfortunately suffers from notorious domain shifts. In this work, we propose a
Data-Oriented Domain Adaptation (DODA) framework to mitigate pattern and
context gaps caused by different sensing mechanisms and layout placements
across domains. Our DODA encompasses virtual scan simulation to imitate
real-world point cloud patterns and tail-aware cuboid mixing to alleviate the
interior context gap with a cuboid-based intermediate domain. The first
unsupervised sim-to-real adaptation benchmark on 3D indoor semantic
segmentation is also built on 3D-FRONT, ScanNet and S3DIS along with 7 popular
Unsupervised Domain Adaptation (UDA) methods. Our DODA surpasses existing UDA
approaches by over 13% on both 3D-FRONT ScanNet and 3D-FRONT
S3DIS. Code will be available
Spin triplet superconducting pairing in doped MoS
Searching for triplet superconductivity has been pursued intensively in a
broad field of material science and quantum information for decades.
Nevertheless, these novel states remain rare. Within a simplified effective
three-orbital model, we reveal a spin triplet pairing in doped MoS by
employing both the finite temperature determinant quantum Monte Carlo approach
and the ground state constrained-phase quantum Monte Carlo method. In a wide
filling region of \avg{n}=0.60-0.80 around charge neutrality \avg{n}=2/3,
the -wave pairing dominates over other symmetries. The pairing
susceptibility strongly increases as the on-site Coulomb interaction increases,
and it is insensitive to spin-orbit coupling.Comment: Accepted for publication as a Regular Article in Physical Review
White-box Compiler Fuzzing Empowered by Large Language Models
Compiler correctness is crucial, as miscompilation falsifying the program
behaviors can lead to serious consequences. In the literature, fuzzing has been
extensively studied to uncover compiler defects. However, compiler fuzzing
remains challenging: Existing arts focus on black- and grey-box fuzzing, which
generates tests without sufficient understanding of internal compiler
behaviors. As such, they often fail to construct programs to exercise
conditions of intricate optimizations. Meanwhile, traditional white-box
techniques are computationally inapplicable to the giant codebase of compilers.
Recent advances demonstrate that Large Language Models (LLMs) excel in code
generation/understanding tasks and have achieved state-of-the-art performance
in black-box fuzzing. Nonetheless, prompting LLMs with compiler source-code
information remains a missing piece of research in compiler testing.
To this end, we propose WhiteFox, the first white-box compiler fuzzer using
LLMs with source-code information to test compiler optimization. WhiteFox
adopts a dual-model framework: (i) an analysis LLM examines the low-level
optimization source code and produces requirements on the high-level test
programs that can trigger the optimization; (ii) a generation LLM produces test
programs based on the summarized requirements. Additionally,
optimization-triggering tests are used as feedback to further enhance the test
generation on the fly. Our evaluation on four popular compilers shows that
WhiteFox can generate high-quality tests to exercise deep optimizations
requiring intricate conditions, practicing up to 80 more optimizations than
state-of-the-art fuzzers. To date, WhiteFox has found in total 96 bugs, with 80
confirmed as previously unknown and 51 already fixed. Beyond compiler testing,
WhiteFox can also be adapted for white-box fuzzing of other complex, real-world
software systems in general
AN IN SITU PRESERVED EARLY CARBONIFEROUS (SERPUKHOVIAN) BRACHIOPOD COMMUNITY IN SOUTHERN GUIZHOU, CHINA
A brachiopod shell bed from the Lower Carboniferous (Serpukhovian) in Guizhou Province of southern China is reported as representing an in situ preserved brachiopod community. The community is characterized by yielding more than 80% complete and articulated specimens preserved in life position and poor size sorting. A new spiriferide species, Weiningia ziyunensis n. sp., is described in the community, which contains the other eight species belonging to six genera. Morphology and preservation analysis of Weiningia ziyunensis n. sp. suggests that it lived in dense clusters attached to living and dead shells and stabilizing its position with thickened posterior shell. Size-frequency distribution and survivorship curve are applied to the population dynamics investigation of this species. Large number of juvenile shells accompanied by high mortality reflects that many juvenile individuals suffered from the limited life space and turbid environment generated by dense clusters. The same high adult mortality is the result of more pressure from neighbors that lead to shell malformation and eventually low feeding and cleaning efficiency, whereas the low senior mortality is attributed to their abilities to cope with these problems. Members of the community show great difference in numeric frequency, with Weiningia ziyunensis n. sp. being one of the dominant species that was characterized by crowded life strategy. By living in dense clusters on Striatifera striata (Fischer de Waldheim, 1837) or other shell fragments, Weiningia ziyunensis n. sp. could resist the water current and gradually expand its population
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